Quantum Entanglement-Based Information Encoding and Decoding via Bio-Inspired Algorithms

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum entanglement-based information encoding and decoding, drawing inspiration from biological neural networks. The core concept leverages the plasticity of synapses and the quantum entanglement properties between neurons to enhance the reliability of information transmission. Specifically, we simulate synaptic plasticity to dynamically modulate entanglement states, employing a biologically-inspired search algorithm to optimize these states for robust encoding and decoding. The algorithm's key innovation lies in the synergistic combination of biological mechanisms with quantum phenomena, offering a potentially more efficient and resilient framework for quantum information processing compared to traditional approaches. We present a theoretical framework outlining the algorithm's operation, detailing the mathematical formulation of the entanglement manipulation process and the optimization strategy. The proposed method holds promise for applications in secure communication, quantum sensing, and potentially, a deeper understanding of the interplay between quantum mechanics and biological systems.

DOI: 10.5281/zenodo.22155261

Related papers

Quantum Entanglement-Based Simulation of Information Encoding and Decoding in Biological Systems

Jincheng Zhang

This research investigates the potential of utilizing quantum entanglement to simulate information encoding and decoding mechanisms within biological neural networks. The core premise is that quantum phenomena, specifically entanglement, may play a more significant role in biological neuronal processing than previously acknowledged. This work proposes a novel approach employing quantum bits (qubits) to represent individual neurons, with connections between neurons established through quantum entanglement. The simulation aims to mimic the processes of synaptic transmission and signal propagation observed in biological neural networks. Mathematical formulations describe the evolution of the entangled qubit states under influence of simulated neuronal inputs, and the measurement processes are modeled to represent the observed output signals. This research offers a theoretical framework for exploring the interplay between quantum mechanics and biological neural systems, potentially leading to a deeper understanding of the fundamental principles governing brain function. The study utilizes a discrete-time simulation approach, allowing for a detailed analysis of the dynamics of the entangled neural network. Key performance indicators, such as the fidelity of information transfer and the robustness of the simulated network, are explored. The results suggest that quantum entanglement can provide a viable model for simulating complex neural processes, warranting further investigation into the potential for quantum computing to revolutionize our understanding of the brain.

Aug 28, 20260 citations

Bio-Inspired Algorithms for Mitigating Quantum Decoherence

Jincheng Zhang

Quantum decoherence remains a significant obstacle to the development of practical quantum computers. The rapid loss of quantum information due to environmental interactions severely limits the duration and fidelity of quantum computations. This paper investigates a novel approach to mitigating decoherence by drawing inspiration from biological neural networks, specifically the mechanisms of long-term potentiation (LTP) and long-term depression (LTD). We propose a bio-inspired algorithm that dynamically adjusts the interactions between quantum bits (qubits) based on simulated synaptic plasticity, aiming to enhance the stability and coherence of quantum systems. The core claim is that mimicking the adaptive nature of biological synapses can effectively counteract decoherence. The key mechanism involves translating LTP and LTD principles into a control strategy for qubit interactions. We present a mathematical framework for quantifying decoherence and demonstrate, through theoretical analysis, the potential of this approach. The proposed algorithm leverages the inherent resilience of biological systems to improve the robustness of quantum computations. Further research will explore the practical implementation of this algorithm in various quantum computing architectures.

Sep 7, 20260 citations

Dynamic Quantum Algorithms Based on Biological Synaptic Plasticity

Jincheng Zhang

This paper explores a novel approach to quantum algorithm design by leveraging the principles of biological synaptic plasticity. The core idea is to mimic the dynamic adjustments observed in neuronal synapses to dynamically optimize and adapt quantum algorithms. We propose a framework where the inherent plasticity of biological systems is translated into a mechanism for parameter tuning and error correction within quantum computations. Specifically, we model synaptic plasticity, such as Hebbian learning and spike-timing-dependent plasticity (STDP), and integrate these models into the optimization process of quantum algorithms. This approach aims to endow quantum algorithms with self-learning and adaptive capabilities, potentially leading to significant improvements in efficiency and robustness. The theoretical framework presented here provides a foundation for developing more resilient and adaptable quantum computing solutions, drawing inspiration from the remarkable adaptability of the human brain. We analyze the potential impact of this dynamic adaptation on various quantum algorithms and outline key challenges and future research directions. ---

Sep 6, 20260 citations

Quantum Computation Based on Neuronal Dynamics

Jincheng Zhang

This paper proposes a novel quantum computing model that leverages the dynamics of neuronal behavior as its foundational building blocks. Unlike conventional quantum computing approaches reliant on purely classical qubits, this model utilizes quantum systems designed to mimic the dynamic changes observed in biological neural networks, specifically at the level of synaptic connections. The core principle involves employing quantum superposition and interference to construct quantum bits (qubits) representing the state of these simulated synapses. Algorithm execution is achieved through precise manipulation, or "quantum tuning," of these qubits. The proposed architecture represents a biologically-inspired paradigm shift in quantum computation, potentially offering advantages in terms of computational efficiency and resilience, particularly for problems exhibiting inherent biological complexity. This work outlines the theoretical framework, key equations, and the fundamental principles driving this new approach to quantum information processing.

Aug 29, 20260 citations

Quantum Computation Based on Biological Neural Networks

Jincheng Zhang

This paper proposes a novel quantum computing architecture inspired by the structure and function of biological neural networks. The core claim is to enhance quantum computing efficiency and fault tolerance by leveraging the inherent properties of biological systems. The mechanism involves modeling quantum bits (qubits) using biologically inspired neuron and synapse models, mimicking their interactions, and utilizing the self-organizing capabilities of neural networks for quantum computation. Unlike traditional quantum computing architectures rooted in classical physics, this approach aims to capitalize on the flexibility and adaptability found within biological systems. The architecture explores concepts such as quantum entanglement and superposition through simulated neural network dynamics, offering a potentially more robust and scalable solution for quantum processing. This work lays the foundation for a biologically-inspired quantum computing paradigm.

Aug 29, 20260 citations

Quantum Entanglement-Based Information Encoding in Neuromorphic Computing

Jincheng Zhang

This paper explores a novel approach to neuromorphic computing by leveraging the unique properties of quantum entanglement. The core idea is to utilize multi-particle quantum entanglement states as the physical connections between artificial neurons, enabling efficient parallel information processing and mimicking biological neural networks. We propose a quantum neuromorphic chip architecture employing multiple superconducting quantum bits (Qubits) engineered to exhibit entanglement. The design focuses on manipulating the coherence time, topological structure, and combined operation of quantum gates to emulate synaptic transmission and learning within biological neural networks. Specifically, we investigate encoding information through entanglement and explore parallel computation facilitated by this quantum phenomenon. Furthermore, we address the challenges of quantum computation by integrating quantum decoherence and quantum error correction techniques. The potential for achieving high-performance, energy-efficient, and biologically inspired computing is discussed, representing a significant departure from traditional approaches and classical neural networks. The proposed framework offers a pathway towards scalable quantum neuromorphic systems with potential applications in areas such as pattern recognition, machine learning, and complex system modeling.

Sep 6, 20260 citations